Scenario provides an AI platform for generating synthetic data tailored for training computer vision models. This platform allows developers to create photorealistic, labeled datasets on demand, overcoming the limitations of real-world data collection. The service accelerates machine learning development cycles by delivering diverse, scalable training environments.
Funding
Funding not disclosed
Founders
Product
Problem
Many computer vision projects struggle with insufficient, costly, or biased labeled data, especially when real-world collection is hazardous, time‑intensive, or fails to capture rare scenarios. This data gap leads to poor model generalization and delayed product rollouts. Additionally, aligning synthetic data with specific deployment environments often requires extensive manual tuning.
Solution
Scenario offers an AI‑driven synthetic data platform that programmatically creates photorealistic, fully labeled image and video datasets. Users define virtual environments through a parametric scene editor or API, specifying objects, lighting, weather, and sensor characteristics to mirror target deployment conditions. The platform’s rendering engine produces high‑fidelity visuals while automatically generating annotations such as bounding boxes, segmentation masks, keypoints, and depth maps. Integrated domain randomization and style‑transfer modules reduce the sim‑to‑real gap, enabling models trained on synthetic data to perform reliably on real inputs. Data generation scales on demand via cloud compute, allowing rapid iteration and large‑scale dataset production without manual labeling effort. Export formats and SDKs support seamless ingestion into common ML pipelines and version‑controlled data repositories.
Target Audience
The primary customers are computer‑vision engineering teams in autonomous driving, robotics, retail analytics, and AR/VR who need large, diverse, and accurately labeled training data for model development and validation.
Features
- Physically based rendering pipeline delivering photorealistic RGB, depth, and infrared outputs
- Parametric scene composer with library of 3D assets, materials, and environmental effects
- Automatic, pixel‑accurate annotation generation for detection, segmentation, pose estimation, and depth sensing
- Domain randomization and neural style transfer to emulate sensor noise, weather, and lighting variations
- RESTful API and Python SDK for programmatic dataset creation and integration with CI/CD workflows
- Cloud‑native scaling with on‑demand GPU clusters and job scheduling for batch generation of millions of frames
- Dataset versioning and metadata tagging compatible with MLflow, DVC, and other data‑ops tools